Developing a Learning Model Based on a Short/Long-term Memory and Application in Signature Verification

机译：基于短/长期记忆和签名验证的应用程序开发学习模型

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We propose a learning model based on a short and long-term memory and a ranking mechanism which manages the transition of reference vectors between two memories. An optimization algorithm is also used to continuously adjust the reference vectors components as well as their distribution. Compared to other learning models like neural networks, the main advantage of the proposed model is no need for a pre-training phase, as well as its hardware-friendly structure. The model was applied in a signature verification task and the classification results were found to be satisfactory.

机译：我们提出了一种基于短期和长期存储器的学习模型和一种排名机制，该排名机制在两个存储器之间管理参考矢量的转变。优化算法还用于连续调整参考向量组件以及它们的分布。与类似神经网络这样的其他学习模型相比，所提出的模型的主要优点是不需要预训练阶段，以及其硬件友好的结构。该模型应用于签名验证任务，并发现分类结果令人满意。

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《International Conference on Artificial Intelligence and Pattern Recognition》|2009年||共7页
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Ali Ahmadi; Hans Jurgen Mattausch; Yoshinori Shirakawa;
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中图分类 TP18-53;
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Developing a Learning Model Based on a Short/Long-term Memory and Application in Signature Verification

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